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1. Investigating the effect of locally available volcanic ash on mechanical and microstructure properties of concrete
2. Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste
3. Enhancing the predictive accuracy of marshall design tests using generative adversarial networks and advanced machine learning techniques
4. Interpretable predictive modeling, sustainability assessment, and cost analysis of cement-based composite containing secondary raw materials
5. Optimizing plastic waste inclusion in paver blocks: Balancing performance, environmental impact, and cost through LCA and economic analysis
6. Performance evaluation of concrete made with plastic waste using multi-expression programming
7. Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners
8. Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag
9. Toward sustainability: Integrating experimental study and data-driven modeling for eco-friendly paver blocks containing plastic waste
10. Soft computing models for prediction of bentonite plastic concrete strength
11. Evaluation of machine learning models for predicting TiO₂ photocatalytic degradation of air contaminants
12. Publisher Correction: Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants (Scientific Reports, (2024), 14, 1, (13688), 10.1038/s41598-024-64486-7)
13. Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches
14. Indirect estimation of resilient modulus (M_r) of subgrade soil: Gene expression programming vs multi expression programming
15. Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers
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